Estimation of Hierarchical Emotion in Mental State Transition Learning Network

In general, emotions are often appeared in the facial expressions, voice pitch, exaggerated gesticulation, and so on. They are outward signals of emotions, internal world in order to serve for human communications. Perlovsky described on aesthetic emotions and analyzed their role within joint functioning of cognition and language. This paper proposes the different method from his idea. The method uses Mental State Transition Network proposed by Ren and Emotion Generation Calculations. Moreover, the transition costs in the network are modified according to the stimulus from external world. The simulation results also are reported.